Granular neural networks with evolutionary interval learning

被引:27
|
作者
Zhang, Yan-Qing [1 ]
Jin, Bo [1 ]
Tang, Yuchun [2 ]
机构
[1] Georgia State Univ, Dept Comp Sci, Atlanta, GA 30302 USA
[2] Secure Comp Corp, Alpharetta, GA 30022 USA
关键词
genetic algorithms; granular computing; granular sets; neural networks; type-2 fuzzy logic; Yin Yang;
D O I
10.1109/TFUZZ.2007.895975
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
To deal with different membership functions of the same linguistic term, a new interval reasoning method using new granular sets is proposed based on Yin Yang methodology. To make interval-valued granular reasoning efficiently and optimize interval membership functions based on training data effectively, a granular neural network (GNN) with a new high-speed evolutionary interval learning is designed. Simulation results in nonlinear function approximation and bioinformatics have shown that the GNN with the evolutionary interval learning is able to extract interval-valued granular rules effectively and efficiently from training data by using the new evolutionary interval learning algorithm.
引用
收藏
页码:309 / 319
页数:11
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